Energy Aware Collaborative Machine Learning on Energy-Harvesting Devices

Qi Hui Sun, Chia Heng Tu

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

Performing machine learning tasks on low end devices enables the development of various smart applications. Especially, these low end devices are often equipped with ultra-low-power microcontroller units (MCUs) that have weak computation power and few memory resources. It is a more challenging work to put these machine learning tasks on those end devices powered by harvested ambient energy, which are often referred to as energy-harvesting (EH) devices, since the unstable ambient energy can lead to the execution failure of the machine learning tasks. This paper proposes an adaptive energy-aware design to coordinate multiple EH devices to accomplish multi-class classification computation. It also leverages the concept of the One-vs-All (OVA) strategy turning a multi-class classification into multiple binary classifications. The experimental results show our work performs better than the widely used round-robin policy and self-greedy policy in consideration of time and energy consumption.

原文English
主出版物標題2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面179-180
頁數2
ISBN(電子)9798350324174
DOIs
出版狀態Published - 2023
事件2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Pingtung, Taiwan
持續時間: 2023 7月 172023 7月 19

出版系列

名字2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings

Conference

Conference2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
國家/地區Taiwan
城市Pingtung
期間23-07-1723-07-19

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 人機介面
  • 資訊系統
  • 資訊系統與管理
  • 電氣與電子工程
  • 媒體技術
  • 儀器

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